The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Sequential Karhunen-Loeve basis extraction and its application to images
Open accessAbstract
The Karhunen-Loeve (KL) transform is an optimal method for approximating a set of vectors or images, which was used in image processing and computer vision for several tasks such as face and object recognition. Its computational demands and its batch calculation nature have limited its application. Here we present a new, sequential algorithm for calculating the KL basis, which is faster in typical applications and is especially advantageous for image sequences: the KL basis calculation is done with much lower delay and allows for dynamic updating of image databases. Systematic tests of the implemented algorithm show that these advantages are indeed obtained with the same accuracy available from batch KL algorithms.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
Introduction to Statistical Pattern Recognition
(1990) · Elsevier eBooks · doi:10.1016/c2009-0-27872-x · Cited by 11,031
Face recognition using eigenfaces
Turk, M. A., & Pentland, A. P. (2002) · Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1991.139758 · Cited by 5,197
Time Series: Data Analysis and Theory.
Koopmans, L. H., & Brillinger, D. R. (1976) · Journal of the American Statistical Association · doi:10.2307/2285347 · Cited by 2,637
Low-dimensional procedure for the characterization of human faces
Sirovich, L., & Kirby, M. (1987) · Journal of the Optical Society of America A · doi:10.1364/josaa.4.000519 · Cited by 2,311
Visual learning and recognition of 3-d objects from appearance
Murase, H., & Nayar, S. K. (1995) · International Journal of Computer Vision · doi:10.1007/bf01421486 · Cited by 1,881
Using Linear Algebra for Intelligent Information Retrieval
Berry, M. W., Dumais, S. T., & O’Brien, G. W. (1995) · SIAM Review · doi:10.1137/1037127 · Cited by 1,481
Probabilistic visual learning for object representation
Moghaddam, B., & Pentland, A. (1997) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.598227 · Cited by 1,441
EigenTracking: Robust Matching and Tracking of Articulated Objects Using a View-Based Representation
Black, M. J., & Jepson, A. D. (1998) · International Journal of Computer Vision · doi:10.1023/a:1007939232436 · Cited by 1,029
Real-time tracking of image regions with changes in geometry and illumination
Hager, G. D., & Belhumeur, P. N. (1996) · Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.1996.517104 · Cited by 222
Cited by
Incremental Learning for Robust Visual Tracking
Ross, D. A., Lim, J., Lin, R.-S., & Yang, M.-H. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0075-7 · Cited by 3,061
A survey of appearance models in visual object tracking
Li, X., Hu, W., Shen, C., Zhang, Z., Dick, A., & Hengel, A. V. D. (2013) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2508037.2508039 · Cited by 705
Incremental Face Alignment in the Wild
Asthana, A., Zafeiriou, S., Cheng, S., & Pantic, M. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.240 · Cited by 450
Unsupervised Multiway Data Analysis: A Literature Survey
Acar, E., & Yener, B. (2009) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2008.112 · Cited by 434
Energy-Efficient Multi-Mode Compressed Sensing System for Implantable Neural Recordings
Suo, Y., Zhang, J., Xiong, T., Chin, P. S., Etienne-Cummings, R., & Tran, T. D. (2014) · IEEE Transactions on Biomedical Circuits and Systems · doi:10.1109/tbcas.2014.2359180 · Cited by 47
Metadata
- Authors
- Levey, A., & Lindenbaum, M.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2000
- Volume
- 9
- Issue
- 8
- Pages
- 1371-1374
- ISSN
- 1057-7149 (ISSN-L)
- OpenAlex
- W2133140216
- Cited by
- 377
- References
- 10
- FWCI
- 4.38
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Sequential Karhunen-Loeve basis extraction and its application to images. https://app.cochat.ai/papers/10.1109/83.855432
- The paper
- Levey, A., & Lindenbaum, M. (2000). Sequential Karhunen-Loeve basis extraction and its application to images. IEEE Transactions on Image Processing. https://doi.org/10.1109/83.855432
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
